<ul><li>Contemporary large language models (LLMs) like GPT-4 and Llama, combined with molecular structure embeddings, enable accurate prediction of polymer properties.</li><li>PolyLLMem, a multimodal architecture, integrates text embeddings from Llama 3 with molecular structure embeddings derived from Uni-Mol.</li><li>Low-rank adaptation (LoRA) layers are incorporated to refine the embeddings based on limited polymer dataset, enhancing their chemical relevance.</li><li>PolyLLMem's performance is comparable to graph-based and transformer-based models, even with limited training data, accelerating the discovery of advanced polymeric materials.</li></ul>

Multimodal machine learning with large language embedding model for polymer property prediction

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